Journal: Optics Express

Article Title: Noise-robust latent vector reconstruction in ptychography using deep generative models

doi: 10.1364/oe.513556

Figure Lengend Snippet: Fig. 1. (A) Schematic of the optical setup used for ptychography. A 500-µm pinhole is illuminated with coherent light at λ = 561 nm and relayed onto the object using a 2-lens system. The object is moved laterally through the beam using a computer-controlled XY stage, and a CMOS camera sensor records the diffraction intensities 6.5 cm downstream from the object. (B) Diagram of the Automatic Differentiation Ptychography (ADP) framework, which models the physical system beginning from the object illumination. In the conventional mode, the object is represented by complex-valued pixels. With a pre-trained autoencoder for a specific class of objects, the decoder can be integrated into the ADP framework as a deep generative model, allowing the object to be represented as a latent vector and significantly reducing the number of free parameters.

Article Snippet: Diffraction patterns are recorded 6.5 cm downstream of the object using a CMOS camera sensor (acA2440-35um, Basler) with a pixel size of 3.45 μm and 1024×1024 total pixels.

Techniques: Plasmid Preparation

Journal: Optics Express

Article Title: Noise-robust latent vector reconstruction in ptychography using deep generative models

doi: 10.1364/oe.513556

Figure Lengend Snippet: Fig. 4. Comparison of ptychographic amplitude image reconstruction results under varying signal-to-noise ratios (SNR). The top row displays stacks of diffraction patterns used for reconstruction, with exposure times decreasing from left to right, leading to a corresponding decrease in SNR. The second row presents results from conventional reconstruction. Subsequent rows feature latent vector reconstructions using a pre-trained deep generative model, first trained on the full MNIST dataset and secondly on a filtered MNIST dataset containing only images resembling the digit ’4’.

Article Snippet: Diffraction patterns are recorded 6.5 cm downstream of the object using a CMOS camera sensor (acA2440-35um, Basler) with a pixel size of 3.45 μm and 1024×1024 total pixels.

Techniques: Comparison, Plasmid Preparation

Journal: Optics Express

Article Title: Noise-robust latent vector reconstruction in ptychography using deep generative models

doi: 10.1364/oe.513556

Figure Lengend Snippet: Fig. 6. Visualization of the optimization loss landscapes for different scenarios. α and β are coefficients for the two leading principal components of the latent space used for ptychographic reconstruction. (A) The landscape for high signal-to-noise ratio (SNR) and training on the full MNIST dataset. (B) The landscape when reconstructing from low-SNR (high-noise) diffraction data. (C) The landscape after training the deep generative model on a filtered MNIST dataset containing only the digit ’4’. (D) The landscape when optimizing using a Poisson-only loss function at high SNR, for comparison with the mixed Poisson-Gaussian loss from all other panels.

Article Snippet: Diffraction patterns are recorded 6.5 cm downstream of the object using a CMOS camera sensor (acA2440-35um, Basler) with a pixel size of 3.45 μm and 1024×1024 total pixels.

Techniques: Comparison